
"""simple docstring"""



from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments

"""simple docstring"""



import argparse

import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor

from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor


def 			_UpperCamelCase						(    _A   )     ->	str:





    """simple docstring"""

    _UpperCAmelCase        = SwinaSRConfig()

    if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
        _UpperCAmelCase        = 4
    elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
        _UpperCAmelCase        = 4
        _UpperCAmelCase        = 4_8
        _UpperCAmelCase        = """pixelshuffle_aux"""
    elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
        _UpperCAmelCase        = [6, 6, 6, 6]
        _UpperCAmelCase        = 6_0
        _UpperCAmelCase        = [6, 6, 6, 6]
        _UpperCAmelCase        = """pixelshuffledirect"""
    elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
        _UpperCAmelCase        = 4
        _UpperCAmelCase        = """nearest+conv"""
    elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
        _UpperCAmelCase        = 1
        _UpperCAmelCase        = 1
        _UpperCAmelCase        = 1_2_6
        _UpperCAmelCase        = 7
        _UpperCAmelCase        = 255.0
        _UpperCAmelCase        = """"""

    return config


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Tuple:





    """simple docstring"""

    if "patch_embed.proj" in name and "layers" not in name:
        _UpperCAmelCase        = name.replace("""patch_embed.proj"""       ,   """embeddings.patch_embeddings.projection"""   )
    if "patch_embed.norm" in name:
        _UpperCAmelCase        = name.replace("""patch_embed.norm"""       ,   """embeddings.patch_embeddings.layernorm"""   )
    if "layers" in name:
        _UpperCAmelCase        = name.replace("""layers"""       ,   """encoder.stages"""   )
    if "residual_group.blocks" in name:
        _UpperCAmelCase        = name.replace("""residual_group.blocks"""       ,   """layers"""   )
    if "attn.proj" in name:
        _UpperCAmelCase        = name.replace("""attn.proj"""       ,   """attention.output.dense"""   )
    if "attn" in name:
        _UpperCAmelCase        = name.replace("""attn"""       ,   """attention.self"""   )
    if "norm1" in name:
        _UpperCAmelCase        = name.replace("""norm1"""       ,   """layernorm_before"""   )
    if "norm2" in name:
        _UpperCAmelCase        = name.replace("""norm2"""       ,   """layernorm_after"""   )
    if "mlp.fc1" in name:
        _UpperCAmelCase        = name.replace("""mlp.fc1"""       ,   """intermediate.dense"""   )
    if "mlp.fc2" in name:
        _UpperCAmelCase        = name.replace("""mlp.fc2"""       ,   """output.dense"""   )
    if "q_bias" in name:
        _UpperCAmelCase        = name.replace("""q_bias"""       ,   """query.bias"""   )
    if "k_bias" in name:
        _UpperCAmelCase        = name.replace("""k_bias"""       ,   """key.bias"""   )
    if "v_bias" in name:
        _UpperCAmelCase        = name.replace("""v_bias"""       ,   """value.bias"""   )
    if "cpb_mlp" in name:
        _UpperCAmelCase        = name.replace("""cpb_mlp"""       ,   """continuous_position_bias_mlp"""   )
    if "patch_embed.proj" in name:
        _UpperCAmelCase        = name.replace("""patch_embed.proj"""       ,   """patch_embed.projection"""   )

    if name == "norm.weight":
        _UpperCAmelCase        = """layernorm.weight"""
    if name == "norm.bias":
        _UpperCAmelCase        = """layernorm.bias"""

    if "conv_first" in name:
        _UpperCAmelCase        = name.replace("""conv_first"""       ,   """first_convolution"""   )

    if (
        "upsample" in name
        or "conv_before_upsample" in name
        or "conv_bicubic" in name
        or "conv_up" in name
        or "conv_hr" in name
        or "conv_last" in name
        or "aux" in name
    ):
        # heads
        if "conv_last" in name:
            _UpperCAmelCase        = name.replace("""conv_last"""       ,   """final_convolution"""   )
        if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
            if "conv_before_upsample.0" in name:
                _UpperCAmelCase        = name.replace("""conv_before_upsample.0"""       ,   """conv_before_upsample"""   )
            if "upsample.0" in name:
                _UpperCAmelCase        = name.replace("""upsample.0"""       ,   """upsample.convolution_0"""   )
            if "upsample.2" in name:
                _UpperCAmelCase        = name.replace("""upsample.2"""       ,   """upsample.convolution_1"""   )
            _UpperCAmelCase        = """upsample.""" + name
        elif config.upsampler == "pixelshuffledirect":
            _UpperCAmelCase        = name.replace("""upsample.0.weight"""       ,   """upsample.conv.weight"""   )
            _UpperCAmelCase        = name.replace("""upsample.0.bias"""       ,   """upsample.conv.bias"""   )
        else:
            pass
    else:
        _UpperCAmelCase        = """swin2sr.""" + name

    return name


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	int:





    """simple docstring"""

    for key in orig_state_dict.copy().keys():
        _UpperCAmelCase        = orig_state_dict.pop(_A   )

        if "qkv" in key:
            _UpperCAmelCase        = key.split("""."""   )
            _UpperCAmelCase        = int(key_split[1]   )
            _UpperCAmelCase        = int(key_split[4]   )
            _UpperCAmelCase        = config.embed_dim

            if "weight" in key:
                _UpperCAmelCase        = val[:dim, :]
                _UpperCAmelCase        = val[dim : dim * 2, :]
                _UpperCAmelCase        = val[-dim:, :]
            else:
                _UpperCAmelCase        = val[:dim]
                _UpperCAmelCase        = val[dim : dim * 2]
                _UpperCAmelCase        = val[-dim:]
            pass
        else:
            _UpperCAmelCase        = val

    return orig_state_dict


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A   )     ->	List[Any]:





    """simple docstring"""

    _UpperCAmelCase        = get_config(_A   )
    _UpperCAmelCase        = SwinaSRForImageSuperResolution(_A   )
    model.eval()

    _UpperCAmelCase        = torch.hub.load_state_dict_from_url(_A       ,   map_location="""cpu"""   )
    _UpperCAmelCase        = convert_state_dict(_A       ,   _A   )
    _UpperCAmelCase     ,_UpperCAmelCase        = model.load_state_dict(_A       ,   strict=_A   )

    if len(_A   ) > 0:
        raise ValueError("""Missing keys when converting: {}""".format(_A   )   )
    for key in unexpected_keys:
        if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
            raise ValueError(F"""Unexpected key {key} in state_dict"""   )

    # verify values
    _UpperCAmelCase        = """https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"""
    _UpperCAmelCase        = Image.open(requests.get(_A       ,   stream=_A   ).raw   ).convert("""RGB"""   )
    _UpperCAmelCase        = SwinaSRImageProcessor()
    # pixel_values = processor(image, return_tensors="pt").pixel_values

    _UpperCAmelCase        = 1_2_6 if """Jpeg""" in checkpoint_url else 2_5_6
    _UpperCAmelCase        = Compose(
        [
            Resize((image_size, image_size)   ),
            ToTensor(),
            Normalize(mean=[0.485, 0.456, 0.406]       ,   std=[0.229, 0.224, 0.225]   ),
        ]   )
    _UpperCAmelCase        = transforms(_A   ).unsqueeze(0   )

    if config.num_channels == 1:
        _UpperCAmelCase        = pixel_values[:, 0, :, :].unsqueeze(1   )

    _UpperCAmelCase        = model(_A   )

    # assert values
    if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
        _UpperCAmelCase        = torch.Size([1, 3, 5_1_2, 5_1_2]   )
        _UpperCAmelCase        = torch.tensor(
            [[-0.7_087, -0.7_138, -0.6_721], [-0.8_340, -0.8_095, -0.7_298], [-0.9_149, -0.8_414, -0.7_940]]   )
    elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
        _UpperCAmelCase        = torch.Size([1, 3, 1_0_2_4, 1_0_2_4]   )
        _UpperCAmelCase        = torch.tensor(
            [[-0.7_775, -0.8_105, -0.8_933], [-0.7_764, -0.8_356, -0.9_225], [-0.7_976, -0.8_686, -0.9_579]]   )
    elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
        # TODO values didn't match exactly here
        _UpperCAmelCase        = torch.Size([1, 3, 1_0_2_4, 1_0_2_4]   )
        _UpperCAmelCase        = torch.tensor(
            [[-0.8_035, -0.7_504, -0.7_491], [-0.8_538, -0.8_124, -0.7_782], [-0.8_804, -0.8_651, -0.8_493]]   )
    elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
        _UpperCAmelCase        = torch.Size([1, 3, 5_1_2, 5_1_2]   )
        _UpperCAmelCase        = torch.tensor(
            [[-0.7_669, -0.8_662, -0.8_767], [-0.8_810, -0.9_962, -0.9_820], [-0.9_340, -1.0_322, -1.1_149]]   )
    elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
        _UpperCAmelCase        = torch.Size([1, 3, 1_0_2_4, 1_0_2_4]   )
        _UpperCAmelCase        = torch.tensor(
            [[-0.5_238, -0.5_557, -0.6_321], [-0.6_016, -0.5_903, -0.6_391], [-0.6_244, -0.6_334, -0.6_889]]   )

    assert (
        outputs.reconstruction.shape == expected_shape
    ), F"""Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}"""
    assert torch.allclose(outputs.reconstruction[0, 0, :3, :3]       ,   _A       ,   atol=1e-3   )
    print("""Looks ok!"""   )

    _UpperCAmelCase        = {
        """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth""": (
            """swin2SR-classical-sr-x2-64"""
        ),
        """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth""": (
            """swin2SR-classical-sr-x4-64"""
        ),
        """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth""": (
            """swin2SR-compressed-sr-x4-48"""
        ),
        """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth""": (
            """swin2SR-lightweight-x2-64"""
        ),
        """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth""": (
            """swin2SR-realworld-sr-x4-64-bsrgan-psnr"""
        ),
    }
    _UpperCAmelCase        = url_to_name[checkpoint_url]

    if pytorch_dump_folder_path is not None:
        print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""   )
        model.save_pretrained(_A   )
        print(F"""Saving image processor to {pytorch_dump_folder_path}"""   )
        processor.save_pretrained(_A   )

    if push_to_hub:
        model.push_to_hub(F"""caidas/{model_name}"""   )
        processor.push_to_hub(F"""caidas/{model_name}"""   )


if __name__ == "__main__":
 a       :						Optional[int]       		=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--checkpoint_url''',
     default='''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth''',
     type=str,
     help='''URL of the original Swin2SR checkpoint you\'d like to convert.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
 )
 parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the converted model to the hub.''')

 a       :						str       		=		parser.parse_args()
 convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)